MétaCan
Menu
Back to cohort

Hydrocracking of Athabasca VR Using NiO-WO<sub>3</sub> Zeolite-Based Catalysts

2018· article· en· W2785053364 on OpenAlexafffund
Thomas Kaminski, Shaheen Fatima Anis, Maen M. Husein, Raed Hashaikeh

Bibliographic record

VenueEnergy & Fuels · 2018
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsZeoliteCrackingCatalysisCokeFluid catalytic crackingDistillationChemical engineeringAutoclaveYield (engineering)ChemistryResidue (chemistry)Endothermic processPyrolysisMaterials scienceMetallurgyOrganic chemistryAdsorption

Abstract

fetched live from OpenAlex

Hydrocracking of Athabasca vacuum residue (AVR) was carried out in an autoclave using particle and fiber forms of NiO-WO 3 zeolite-supported catalyst. AVR hydrocracking was performed at 400 °C at low and high H 2 pressure of 70 and 365 psi, together with the corresponding control thermal cracking runs. The yield of the different products and the quality of the upgraded liquid was used to assess the catalyst performance. Similarity among energy consumption for the different samples suggested major thermal cracking endothermic reactions. In general, the catalytic runs provided better quality maltene product, whereas better quality product oil was only attained at high pressure. The catalytic runs at low H 2 pressure gave the highest yield of combined asphaltenes and toluene insolubles. This yield, on the other hand, was the lowest for the fiber form at high H 2 pressure. Simulated distillation results captured the superior performance of the fiber catalyst at high H 2 pressure and showed ∼50% conversion of the residue. On the other hand, the zeolite particles showed poor performance at high pressure with only ∼30% residue conversion.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.237
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations27
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueEnergy & FuelsSame topicPetroleum Processing and AnalysisFrench-language works237,207